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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
Utility Working Conference and Vendor Technology Expo
August 8–11, 2021
Marco Island, FL|JW Marriott Marco Island
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Nuclear Science and Engineering
Fusion Science and Technology
Bumpy roads lead to beautiful places
Per Nuclear News tradition, this month’s issue is dedicated to highlighting our nuclear technology supply chain. U.S. nuclear suppliers have certainly seen their share of challenges in the last decade or so. The widely anticipated “Nuclear Renaissance” of the early 2000s gave way to Fukushima, then a wavelet of plant closures that ANS President Steve Nesbit addresses in his column on page 15 of the August 2021 issue of Nuclear News.
However, the nuclear narrative has taken on a more positive tone of late. Significant federal investments in advanced nuclear energy systems, coupled with a broader recognition of the need to decarbonize, has stoked excitement for a new generation of U.S. technology on the verge of scaled commercial deployment by the end of the decade. Hopefully, in the words of Washington Nationals manager Davey Martinez, whose team went from a 19–32 record to World Series champs in 2019, “Bumpy roads lead to beautiful places.”
Lei Jin, Kaushik Banerjee
Nuclear Science and Engineering | Volume 194 | Number 3 | March 2020 | Pages 190-206
Technical Paper | dx.doi.org/10.1080/00295639.2019.1678104
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) simulation is used to solve the eigenvalue form of the Boltzmann transport equation to estimate various parameters such as fuel pin flux distributions that are crucial for the safe and efficient operation of nuclear systems (e.g., a nuclear reactor). Monte Carlo eigenvalue simulation uses a sample mean over many stationary cycles (iterations) to estimate various parameters important to nuclear systems. A variance estimate of the sample mean is often used for calculating the confidence intervals. However, MC eigenvalue simulation variance estimators that ignore the intercycle correlation underestimate the true variance of the estimated quantity. This paper presents novel data-adaptive approaches based on a simple autoregressive (AR) model and sigmoid functions to improve MC variance estimation. The standard MC sample-based variance estimator (or naïve estimator) and the spectral density–based MC variance estimator are enhanced by adding data-adaptive components that reduce their bias and improve performance. By investigating the frequency pattern of the AR(1) (order 1) model, two adaptive spectral estimators and one adaptive naïve estimator are proposed. The proposed estimators manifest superior performance when applied to three test problems compared to the standard spectral density–based estimator previously introduced by the authors. These new estimators are straightforward, as they use online algorithms and do not require storage of tallies from all active cycles.